A threshold theory of discretionary interaction behavior

There presently exists a gap between aggregate and disaggregate approaches to modeling spatial interaction behavior. This paper proposes a strategy for filling this gap by focusing on specific types of interaction behavior which share certain common characteristics. In the present case, a simple threshold theory is developed which establishes a link between various types of discretionary interaction behavior by individuals and the resulting macro distribution of interaction frequencies. In particular, it is shown that if the statistical population of potential interaction situations satisfies certain independence conditions over space, then the resulting macro interaction frequencies are always Poisson distributed with mean frequency levels representable by a classical gravity model.     

From cause to effect and from effect to cause

The theory of probability employs a deductive method of thinking which traces effect from cause. Statistics uses an inductive method of thinking and tries to trace cause from effect. This noble goal can be successfully achieved if and only if a researcher deals with a homogeneous data set. A homogeneous data set is generated logically and then it is to be tested statistically. The homogeneity principle bridges the gap between probability theory and applied statistics and makes statistics as precise as the theory of probability. In this role statistics opens new horizons for estimating parameters when the central limit theorem is not applied.     

The research-practice gap as a pragmatic knowledge boundary

Existing discourse on the research-practice gap underplays the role of practitioners and assumes that the existence of the gap is due primarily to deficiencies in theory. This conceptual paper problematizes this assumption and explores in practice why practitioners have not been able to harness and apply insights from organisation theory (OT) and information systems (IS) research . Drawing on the concept of knowledge boundaries, several key arguments are made. Firstly, a pragmatic knowledge boundary divides academics and practitioners. Secondly, the novelty of OT and IS research at this knowledge boundary hinders practitioners' ability to constructively assess it, which results in contradictions and discourages its application in practice. Finally, when academics and practitioners collaborate to promote deeper engagement, and when effective boundary-spanning objects are used, there are signs that the research-practice gap can be overcome. These arguments are illustrated through a case-study of employees in a medium-sized IT consulting company.     

A Turnpike Theorem in the Closed Dynamic Leontief Model with a Singular Matrix of Capital Coefficients

The paper refers to the well-known Tsukui turnpike theorem on convergence of optimal growth trajectories in the closed dynamic Leontief model to the maximum balanced growth trajectory, called turnpike. In the original proof of this theorem, the assumption that the matrix B of capital coefficients is non-singular plays an essential role. For many reasons this assumption, very convenient for theoretical analysis, is not always satisfied in input-output systems built for empirical purposes. This paper fills the gap between theory and empirical studies, presenting a proof that convergence of optimal trajectories towards the turnpike is also a characteristic feature of the closed Leontief model in the case when matrix B is singular. The general idea of the proof is based on the approximation of a singular matrix B by an infinite sequence of non-singular matrices.     

A note on the existence of CAPM equilibria with homogeneous cumulative prospect theory preferences

This note identifies and fixes a minor gap in Proposition 1 in Barberis and Huang (Am Econ Rev 98(5):2066–2100, 2008). Assuming homogeneous cumulative prospect theory decision makers, we show that CAPM is a necessary (though not sufficient) condition that must hold in equilibrium. We support our results with numerical examples where security prices become negative.     

Revealed Preference and Location Choice

The assumption that population and structural densities are defined over parcels of land with zero area is problematic when interpreting the standard residential land use model as approximating a large finite economy. This paper extends revealed preference theory to depict a spatial economy with land parcels of finite area. The model generalizes the standard utility function approach, allowing for many consumer types with different tastes and incomes and labor-leisure choice, to show that the most important predictions of the standard theory hold in the general finite framework studied here.     

A partial order toolbox for building synthetic indicators of sustainability with ordinal data

This paper designs and implements a statistical toolbox for the construction of synthetic indicators of sustainability and rankings of multidimensional systems of ordinal variables. The toolbox employs results from partial order theory to provide a purely ordinal way to perform the classical steps of indicator construction (i.e., with neither quantification nor aggregation of the input variables) and thus fills a gap in the statistical literature. “Ordinal” non-aggregative procedures are developed to score statistical units and assess the reliability of the final rankings as well as account for exogenous information relating to variable importance. The toolbox is introduced and shown in action through the real example of Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions), specifically as it relates to Political Pluralism and Participation in North and South American countries.     

The new approach in the analysis of 2×2 tables

Conventional statistical analysis of contingency tables is logically untenable. It rejects the knowledge of the prior model of cause, and it tests the null hypothesis of independence which cannot be proved to be true or false. Our approach is based on the knowledge of the prior model of cause, and we test the hypothesis of significant dependence. This hypothesis can be proved to be true or false if data are representative of the homogeneous invisible population.     

Road to recovery: Managing an epidemic

Without widespread immunization, the road to recovery from the current COVID-19 lockdowns will optimally follow a path that finds the difficult balance between the social and economic benefits of liberty and the toll from the disease. We provide an approach that combines epidemiology and economic models, taking as given that the maximum capacity of the healthcare system imposes a constraint that must not be exceeded. Treating the transmission rate as a decreasing function of the severity of the lockdown, we first determine the minimal lockdown that satisfies this constraint using an epidemiology model with a homogeneous population to predict future demand for healthcare. Allowing for a heterogeneous population, we then derive the optimal lockdown policy under the assumption of homogeneous mixing and show that it is characterized by a bang–bang solution. Possibilities such as the capacity of the healthcare system increasing or a vaccine arriving at some point in the future do not substantively impact the dynamically optimal policy until such an event actually occurs.     

Robust likelihood inferences about regression parameters for general bivariate continuous data

This paper introduces a way of modifying the bivariate normal likelihood function. One can use the adjusted likelihood to generate valid likelihood inferences for the regression parameter of interest, even if the bivariate normal assumption is fallacious. The retained asymptotic legitimacy requires no knowledge of the true underlying joint distributions so long as their second moments exist. The extension to the multivariate situations is straightforward in theory and yet appears to be arduous computationally. Nevertheless, it is illustrated that the implementation of this seemingly sophisticated procedure is almost effortless needing only outputs from existing statistical software. The efficacy of the proposed parametric approach is demonstrated via simulations.     

Estimating a frequency unseen: an application to ornithology

The second author is involved in a capture–mark–recapture study of some wader species. Part of his program deals with resight observations. On a particular day he visually inspects a fairly stable population to identify the ringed birds by reading their ring-number. Some ringed birds will be missed, so observations are repeated on other days. The issue of main interest is whether, after some repetitions, we can be sufficiently sure that all the ringed birds in the population have been identified or, equivalently, that the frequency of unseen birds is zero.
Most current theory is concerned with an asymptotic setting. In our 'exact' context the emphasis is on the determination of the 'probability' that the frequency of unseen birds is zero. This issue is settled by considering the more general problem of 'estimating' the frequency of the unseen birds by providing a predictive inference in the form of a probability distribution. We develop methods of inference based on the assumption of a bird-independent probability p _i of identifying a ringed bird on day i , as well as without this assumption. In Section 5 we critically examine these approaches.     

Non-homogeneous servers in emergency medical systems: Practical applications using the hypercube queueing model

In this paper, we study the effects of considering homogeneous versus non-homogeneous servers in applications of the hypercube queueing model. This is important since approximate methods available for solving the model for homogeneous servers are computationally much less time-consuming than the exact methods required for the non-homogeneous case. Illustrative examples are initially presented to show the degree to which using homogeneous versus non-homogeneous servers can differ. Then, two ambulance deployment applications dealing with Brazilian emergency medical systems, in a city and along a highway, are analyzed. The basic operational characteristics of non-homogeneous systems were compared to the respective predictions produced under the simplifying assumption of homogeneous servers. It was found that, even when the degree of non-homogeneity of the servers is not highly significant, homogeneity may lead to poor predictions of the actual operational characteristics of non-homogeneous systems.     

A Predictive Estimator of the Mean with Missing Data

One of the most difficult problems confronting investigators who analyze data from surveys is how treat missing data. Many statistical procedures can not be used immediately if any values are missing. This paper considers the problem of estimating the population mean using auxiliary information when some observations on the sample are missing and the population mean of the auxiliary variable is not available. We use tools of classical statistical estimation theory to find a suitable estimator. We study the model and design properties of the proposed estimator. We also report the results of a broad-based simulation study of the efficiency of the estimator, which reveals very promising results.     

Product-to-Product Tables via Product-Technology with No Negative Flows

This paper presents and evaluates a way of making product-to-product tables from Use and Make matrices that are of immediate relevance to any statistical office that makes input-output tables. Two ways of making a product-to-product table are in common practice: one based on the product-technology assumption and the other on the industry-technology assumption. The industry-technology assumption is recognized as highly implausible but is often used because the product-technology assumption frequently leads to small negative flows which make no economic sense. This paper shows how a slight adjustment in the product-technology assumption leads to an algorithm that is certain to avoid negative flows yet keeps close to the spirit of the product-technology idea. Some details of the application of this method to the USA table for 1992 are reported. Similar applications to every American table since 1958 have given consistently sensible results. A computer program for the method is available.     

A Two‐Step Procedure to Measure Representativeness of Internet Data Sources

So far, statistics has mainly relied on information collected from censuses and sample surveys, which are used to produce statistics about selected characteristics of the population. However, because of cost cuts and increasing non‐response in sample surveys, statisticians have started to search for new sources of information, such as registers, Internet data sources (IDSs, i.e. web portals) or big data. Administrative sources are already used for purposes of official statistics, while the suitability of the latter two sources is currently being discussed in the literature. Unfortunately, only a few papers devoted to statistical theory point out methodological problems related to the use of IDSs, particularly in the context of survey methodology. The unknown generation mechanism and the complexity of such data are often neglected in view of their size. Hence, before IDSs can be used for statistical purposes, especially for official statistics, they need to be assessed in terms of such fundamental issues as representativeness, non‐sampling errors or bias. The paper attempts to fill the first gap by proposing a two‐step procedure to measure representativeness of IDSs. The procedure will be exemplified using data about the secondary real estate market in Poland.     

论短期经营决策的假设前提

本文认为,管理会计假设是一个多层次性的系统理论,包括基本假设、衍生假设和具体假设。短期经营决策假设是管理会计假设系统理论的子系统,由衍生假设和具体假设构成。对短期经营决策假设进行研究,可以为短期经营决策方法的应用和拓展奠定基础,丰富现代管理会计理论内容。     

Heterogeneity in states and urban structure

The purpose of this paper is to relax the standard assumption of static urban economic theory concerning perfect homogeneity in tastes of individuals belonging to the same class. We study the impact of heterogeneity in tastes on urban structure using the simplest meaningful clone of the standard model: all arguments are presented in the context of a metropolitan area which is partitioned in two homogeneous zones, centre and suburd. Given that individuals have perfect knowledge, heterogeneity in tastes may affect equilibrium in two ways. Namely, individuals may differ with respect to their location decision and, at a particular location, they may differ with respect to how they allocate their initial resources. The strategy adopted here is to study these two effects in isolation.     

ENJOYING THE QUIET LIFE UNDER DEREGULATION? NOT QUITE

Most empirical studies in the banking literature assume that the alternative profit function is linearly homogeneous in input prices. We show that such an assumption is theoretically unwarranted and that its use may yield misleading results. We use Koetter et al. (Review of Economics and Statistics 2012; 94 (2): 462–480) as a benchmark to showcase how empirical results can be sensitive to the linear homogeneity assumption. Contrary to Koetter et al., we find a positive relation between market power and profit efficiency when this assumption is dropped. This relation is slightly weakened after the wave of intrastate and interstate deregulation but not enough to support the ‘quiet life’ hypothesis. Copyright © 2014 John Wiley & Sons, Ltd.     

Tests with correct size when instruments can be arbitrarily weak

This paper applies classical exponential-family statistical theory to develop a unifying framework for testing structural parameters in the simultaneous equations model under the assumption of normal errors with known reduced-form variance matrix. The results can be divided into the limited-information and full-information categories. In the limited-information model, it is possible to characterize the entire class of similar tests in a model with only one endogenous explanatory variable. In the full-information framework, this paper proposes a family of similar tests for subsets of endogenous variables’ coefficients. For both limited- and full-information models, there exist power upper bounds for unbiased tests. When the model is just-identified, the Anderson–Rubin, score, and (pseudo) conditional likelihood ratio tests are optimal. When the model is over-identified, the (pseudo) conditional likelihood ratio test has power close to the power envelope when identification is strong.     

Appropriate roles for statistical decision theory and hypothesis testing in model selection: An exposition

Statistical studies in the social sciences often analyze a cross section of communities that are assumed to be homogeneous for certain characteristics. However, the researcher might be interested in investigating the general behavior of all communities as well as particular differences between groups of communities that are spatially separated. This paper suggests, and empirically examines, a procedure which utilizes both statistical decision theory and hypothesis testing and enables us to determine the best model for estimating behavioral relationships for the full sample and for subgroups. The paper examines property crime occurrences in 94 New Jersey suburban communities of Philadelphia utilizing the full sample as well as subgroups within it. The purpose of grouping is to further understand the possible interjurisdictional mobility of urban criminals. The study suggests possible factors that induce urban criminals to commit crimes in the suburbs.